Overview

Dataset statistics

Number of variables25
Number of observations217
Missing cells654
Missing cells (%)12.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory64.3 KiB
Average record size in memory303.4 B

Variable types

Numeric19
Categorical4
Unsupported2

Alerts

art_status has constant value "Positive"Constant
albumin_g_dL has constant value "41.0"Constant
Sex is highly overall correlated with creatinine_umol_L and 1 other fieldsHigh correlation
alt_U_L is highly overall correlated with ast_U_LHigh correlation
ast_U_L is highly overall correlated with alt_U_LHigh correlation
cd4_count_cells_uL is highly overall correlated with lymphocyte_count_10e9_L and 1 other fieldsHigh correlation
creatinine_clearance_mL_min is highly overall correlated with creatinine_umol_LHigh correlation
creatinine_umol_L is highly overall correlated with Sex and 1 other fieldsHigh correlation
hemoglobin_g_dL is highly overall correlated with SexHigh correlation
ldl_cholesterol_mmol_L_consolidated is highly overall correlated with total_cholesterol_mmol_LHigh correlation
lymphocyte_count_10e9_L is highly overall correlated with cd4_count_cells_uL and 1 other fieldsHigh correlation
monocyte_count_10e9_L is highly overall correlated with wbc_count_10e9_LHigh correlation
neutrophil_count_10e9_L is highly overall correlated with wbc_count_10e9_LHigh correlation
total_cholesterol_mmol_L is highly overall correlated with ldl_cholesterol_mmol_L_consolidatedHigh correlation
wbc_count_10e9_L is highly overall correlated with cd4_count_cells_uL and 3 other fieldsHigh correlation
hiv_vl_copies_mL is highly imbalanced (61.9%)Imbalance
BMI (kg/m²) has 217 (100.0%) missing valuesMissing
study_week has 217 (100.0%) missing valuesMissing
cd4_count_cells_uL has 4 (1.8%) missing valuesMissing
albumin_g_dL has 216 (99.5%) missing valuesMissing
BMI (kg/m²) is an unsupported type, check if it needs cleaning or further analysisUnsupported
study_week is an unsupported type, check if it needs cleaning or further analysisUnsupported
eosinophil_count_10e9_L has 5 (2.3%) zerosZeros
basophil_count_10e9_L has 13 (6.0%) zerosZeros

Reproduction

Analysis started2025-11-25 06:49:08.028168
Analysis finished2025-11-25 06:49:20.723598
Duration12.7 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

Age (at enrolment)
Real number (ℝ)

Distinct39
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.663594
Minimum20
Maximum67
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2025-11-25T08:49:20.746599image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile30
Q136
median40
Q347
95-th percentile56.2
Maximum67
Range47
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.0984802
Coefficient of variation (CV)0.19437786
Kurtosis-0.0090547417
Mean41.663594
Median Absolute Deviation (MAD)6
Skewness0.40369989
Sum9041
Variance65.585381
MonotonicityNot monotonic
2025-11-25T08:49:20.792670image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
4016
 
7.4%
3915
 
6.9%
4613
 
6.0%
3413
 
6.0%
3713
 
6.0%
3511
 
5.1%
4211
 
5.1%
3810
 
4.6%
449
 
4.1%
497
 
3.2%
Other values (29)99
45.6%
ValueCountFrequency (%)
201
 
0.5%
251
 
0.5%
262
 
0.9%
271
 
0.5%
282
 
0.9%
291
 
0.5%
307
3.2%
316
2.8%
321
 
0.5%
335
2.3%
ValueCountFrequency (%)
671
 
0.5%
631
 
0.5%
621
 
0.5%
611
 
0.5%
582
 
0.9%
575
2.3%
561
 
0.5%
554
1.8%
547
3.2%
534
1.8%

Sex
Categorical

High correlation 

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size13.2 KiB
Female
153 
Male
64 

Length

Max length6
Median length6
Mean length5.4101382
Min length4

Characters and Unicode

Total characters1174
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowFemale
3rd rowFemale
4th rowFemale
5th rowMale

Common Values

ValueCountFrequency (%)
Female153
70.5%
Male64
29.5%

Length

2025-11-25T08:49:20.840388image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T08:49:20.881281image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
female153
70.5%
male64
29.5%

Most occurring characters

ValueCountFrequency (%)
e370
31.5%
a217
18.5%
l217
18.5%
F153
13.0%
m153
13.0%
M64
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter957
81.5%
Uppercase Letter217
 
18.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e370
38.7%
a217
22.7%
l217
22.7%
m153
16.0%
Uppercase Letter
ValueCountFrequency (%)
F153
70.5%
M64
29.5%

Most occurring scripts

ValueCountFrequency (%)
Latin1174
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e370
31.5%
a217
18.5%
l217
18.5%
F153
13.0%
m153
13.0%
M64
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1174
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e370
31.5%
a217
18.5%
l217
18.5%
F153
13.0%
m153
13.0%
M64
 
5.5%

BMI (kg/m²)
Unsupported

Missing  Rejected  Unsupported 

Missing217
Missing (%)100.0%
Memory size3.4 KiB

study_week
Unsupported

Missing  Rejected  Unsupported 

Missing217
Missing (%)100.0%
Memory size3.4 KiB

cd4_count_cells_uL
Real number (ℝ)

High correlation  Missing 

Distinct194
Distinct (%)91.1%
Missing4
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean669.23944
Minimum90
Maximum1596
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2025-11-25T08:49:20.921334image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum90
5-th percentile210.4
Q1496
median637
Q3885
95-th percentile1136.8
Maximum1596
Range1506
Interquartile range (IQR)389

Descriptive statistics

Standard deviation278.34576
Coefficient of variation (CV)0.41591357
Kurtosis0.1796013
Mean669.23944
Median Absolute Deviation (MAD)184
Skewness0.39364919
Sum142548
Variance77476.362
MonotonicityNot monotonic
2025-11-25T08:49:20.972037image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8943
 
1.4%
5963
 
1.4%
9473
 
1.4%
6202
 
0.9%
4042
 
0.9%
5672
 
0.9%
8472
 
0.9%
6262
 
0.9%
5942
 
0.9%
6512
 
0.9%
Other values (184)190
87.6%
(Missing)4
 
1.8%
ValueCountFrequency (%)
901
0.5%
1211
0.5%
1281
0.5%
1381
0.5%
1541
0.5%
1601
0.5%
1651
0.5%
1771
0.5%
1781
0.5%
1901
0.5%
ValueCountFrequency (%)
15961
0.5%
15011
0.5%
13711
0.5%
13391
0.5%
12541
0.5%
12391
0.5%
12301
0.5%
11871
0.5%
11841
0.5%
11781
0.5%

hiv_vl_copies_mL
Categorical

Imbalance 

Distinct4
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size12.8 KiB
0.0
176 
40.0
39 
41.0
 
1
63.0
 
1

Length

Max length4
Median length3
Mean length3.1889401
Min length3

Characters and Unicode

Total characters692
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.9%

Sample

1st row0.0
2nd row0.0
3rd row40.0
4th row0.0
5th row40.0

Common Values

ValueCountFrequency (%)
0.0176
81.1%
40.039
 
18.0%
41.01
 
0.5%
63.01
 
0.5%

Length

2025-11-25T08:49:21.021018image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T08:49:21.060095image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0176
81.1%
40.039
 
18.0%
41.01
 
0.5%
63.01
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0432
62.4%
.217
31.4%
440
 
5.8%
11
 
0.1%
61
 
0.1%
31
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number475
68.6%
Other Punctuation217
31.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0432
90.9%
440
 
8.4%
11
 
0.2%
61
 
0.2%
31
 
0.2%
Other Punctuation
ValueCountFrequency (%)
.217
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common692
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0432
62.4%
.217
31.4%
440
 
5.8%
11
 
0.1%
61
 
0.1%
31
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII692
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0432
62.4%
.217
31.4%
440
 
5.8%
11
 
0.1%
61
 
0.1%
31
 
0.1%

art_status
Categorical

Constant 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size13.8 KiB
Positive
217 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters1736
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPositive
2nd rowPositive
3rd rowPositive
4th rowPositive
5th rowPositive

Common Values

ValueCountFrequency (%)
Positive217
100.0%

Length

2025-11-25T08:49:21.106354image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T08:49:21.142440image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
positive217
100.0%

Most occurring characters

ValueCountFrequency (%)
i434
25.0%
P217
12.5%
o217
12.5%
s217
12.5%
t217
12.5%
v217
12.5%
e217
12.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1519
87.5%
Uppercase Letter217
 
12.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i434
28.6%
o217
14.3%
s217
14.3%
t217
14.3%
v217
14.3%
e217
14.3%
Uppercase Letter
ValueCountFrequency (%)
P217
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1736
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i434
25.0%
P217
12.5%
o217
12.5%
s217
12.5%
t217
12.5%
v217
12.5%
e217
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1736
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i434
25.0%
P217
12.5%
o217
12.5%
s217
12.5%
t217
12.5%
v217
12.5%
e217
12.5%

hemoglobin_g_dL
Real number (ℝ)

High correlation 

Distinct68
Distinct (%)31.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.53871
Minimum7.6
Maximum17.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2025-11-25T08:49:21.182601image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum7.6
5-th percentile10.5
Q112.5
median13.5
Q314.7
95-th percentile16.3
Maximum17.7
Range10.1
Interquartile range (IQR)2.2

Descriptive statistics

Standard deviation1.753429
Coefficient of variation (CV)0.12951227
Kurtosis0.35834819
Mean13.53871
Median Absolute Deviation (MAD)1.1
Skewness-0.28913593
Sum2937.9
Variance3.0745131
MonotonicityNot monotonic
2025-11-25T08:49:21.232363image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1410
 
4.6%
14.19
 
4.1%
12.57
 
3.2%
12.97
 
3.2%
137
 
3.2%
13.37
 
3.2%
13.86
 
2.8%
15.56
 
2.8%
14.66
 
2.8%
13.26
 
2.8%
Other values (58)146
67.3%
ValueCountFrequency (%)
7.61
 
0.5%
8.61
 
0.5%
91
 
0.5%
9.31
 
0.5%
9.71
 
0.5%
9.91
 
0.5%
10.13
1.4%
10.41
 
0.5%
10.53
1.4%
10.61
 
0.5%
ValueCountFrequency (%)
17.71
 
0.5%
17.61
 
0.5%
17.41
 
0.5%
17.31
 
0.5%
16.93
1.4%
16.51
 
0.5%
16.41
 
0.5%
16.35
2.3%
161
 
0.5%
15.93
1.4%

wbc_count_10e9_L
Real number (ℝ)

High correlation 

Distinct170
Distinct (%)78.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4964055
Minimum2.25
Maximum15.85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2025-11-25T08:49:21.281861image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2.25
5-th percentile3.358
Q14.36
median5.21
Q36.5
95-th percentile8.102
Maximum15.85
Range13.6
Interquartile range (IQR)2.14

Descriptive statistics

Standard deviation1.7174402
Coefficient of variation (CV)0.31246607
Kurtosis8.8783306
Mean5.4964055
Median Absolute Deviation (MAD)0.99
Skewness1.8992561
Sum1192.72
Variance2.9496009
MonotonicityNot monotonic
2025-11-25T08:49:21.334905image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.363
 
1.4%
4.453
 
1.4%
5.743
 
1.4%
5.983
 
1.4%
4.223
 
1.4%
5.212
 
0.9%
4.472
 
0.9%
4.152
 
0.9%
4.22
 
0.9%
7.482
 
0.9%
Other values (160)192
88.5%
ValueCountFrequency (%)
2.251
0.5%
2.281
0.5%
2.41
0.5%
2.481
0.5%
2.51
0.5%
2.971
0.5%
3.151
0.5%
3.171
0.5%
3.211
0.5%
3.251
0.5%
ValueCountFrequency (%)
15.851
0.5%
14.981
0.5%
8.971
0.5%
8.911
0.5%
8.641
0.5%
8.561
0.5%
8.472
0.9%
8.31
0.5%
8.211
0.5%
8.151
0.5%

platelet_count_10e9_L
Real number (ℝ)

Distinct145
Distinct (%)66.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean264.53456
Minimum110
Maximum588
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2025-11-25T08:49:21.385093image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum110
5-th percentile171.8
Q1219
median251
Q3306
95-th percentile385.4
Maximum588
Range478
Interquartile range (IQR)87

Descriptive statistics

Standard deviation71.369474
Coefficient of variation (CV)0.26979262
Kurtosis2.7657312
Mean264.53456
Median Absolute Deviation (MAD)44
Skewness1.1693336
Sum57404
Variance5093.6018
MonotonicityNot monotonic
2025-11-25T08:49:21.436218image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2074
 
1.8%
2344
 
1.8%
2304
 
1.8%
2644
 
1.8%
2454
 
1.8%
1643
 
1.4%
2053
 
1.4%
2373
 
1.4%
2353
 
1.4%
2193
 
1.4%
Other values (135)182
83.9%
ValueCountFrequency (%)
1101
 
0.5%
1341
 
0.5%
1411
 
0.5%
1461
 
0.5%
1481
 
0.5%
1631
 
0.5%
1643
1.4%
1651
 
0.5%
1711
 
0.5%
1721
 
0.5%
ValueCountFrequency (%)
5881
0.5%
5272
0.9%
4771
0.5%
4601
0.5%
4471
0.5%
4221
0.5%
4031
0.5%
3991
0.5%
3901
0.5%
3871
0.5%

neutrophil_count_10e9_L
Real number (ℝ)

High correlation 

Distinct158
Distinct (%)72.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5439539
Minimum0.59
Maximum9.66
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2025-11-25T08:49:21.487653image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.59
5-th percentile1.254
Q11.77
median2.38
Q33.12
95-th percentile4.258
Maximum9.66
Range9.07
Interquartile range (IQR)1.35

Descriptive statistics

Standard deviation1.0653171
Coefficient of variation (CV)0.41876429
Kurtosis8.3370915
Mean2.5439539
Median Absolute Deviation (MAD)0.65
Skewness1.7535956
Sum552.038
Variance1.1349004
MonotonicityNot monotonic
2025-11-25T08:49:21.536418image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.134
 
1.8%
2.723
 
1.4%
2.513
 
1.4%
2.273
 
1.4%
1.633
 
1.4%
2.143
 
1.4%
2.493
 
1.4%
1.623
 
1.4%
1.313
 
1.4%
1.842
 
0.9%
Other values (148)187
86.2%
ValueCountFrequency (%)
0.591
0.5%
0.751
0.5%
0.8181
0.5%
0.972
0.9%
1.071
0.5%
1.081
0.5%
1.111
0.5%
1.151
0.5%
1.221
0.5%
1.231
0.5%
ValueCountFrequency (%)
9.661
0.5%
5.781
0.5%
5.181
0.5%
5.071
0.5%
4.621
0.5%
4.581
0.5%
4.561
0.5%
4.441
0.5%
4.352
0.9%
4.331
0.5%

lymphocyte_count_10e9_L
Real number (ℝ)

High correlation 

Distinct142
Distinct (%)65.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3241475
Minimum0.91
Maximum4.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2025-11-25T08:49:21.582749image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.91
5-th percentile1.322
Q11.81
median2.24
Q32.76
95-th percentile3.572
Maximum4.7
Range3.79
Interquartile range (IQR)0.95

Descriptive statistics

Standard deviation0.71987096
Coefficient of variation (CV)0.30973549
Kurtosis0.65371633
Mean2.3241475
Median Absolute Deviation (MAD)0.46
Skewness0.66184638
Sum504.34
Variance0.5182142
MonotonicityNot monotonic
2025-11-25T08:49:21.633665image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.024
 
1.8%
2.83
 
1.4%
2.33
 
1.4%
2.243
 
1.4%
3.023
 
1.4%
1.963
 
1.4%
2.433
 
1.4%
2.23
 
1.4%
1.793
 
1.4%
1.973
 
1.4%
Other values (132)186
85.7%
ValueCountFrequency (%)
0.911
0.5%
0.921
0.5%
0.931
0.5%
0.971
0.5%
1.031
0.5%
1.071
0.5%
1.151
0.5%
1.171
0.5%
1.251
0.5%
1.261
0.5%
ValueCountFrequency (%)
4.71
0.5%
4.681
0.5%
4.581
0.5%
4.181
0.5%
4.071
0.5%
3.951
0.5%
3.891
0.5%
3.82
0.9%
3.761
0.5%
3.621
0.5%

monocyte_count_10e9_L
Real number (ℝ)

High correlation 

Distinct55
Distinct (%)25.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.40686636
Minimum0.14
Maximum0.94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2025-11-25T08:49:21.684033image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.14
5-th percentile0.208
Q10.32
median0.39
Q30.48
95-th percentile0.632
Maximum0.94
Range0.8
Interquartile range (IQR)0.16

Descriptive statistics

Standard deviation0.1320611
Coefficient of variation (CV)0.32458103
Kurtosis1.267488
Mean0.40686636
Median Absolute Deviation (MAD)0.08
Skewness0.72836662
Sum88.29
Variance0.017440135
MonotonicityNot monotonic
2025-11-25T08:49:21.735685image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3812
 
5.5%
0.3712
 
5.5%
0.3510
 
4.6%
0.368
 
3.7%
0.438
 
3.7%
0.348
 
3.7%
0.396
 
2.8%
0.486
 
2.8%
0.316
 
2.8%
0.446
 
2.8%
Other values (45)135
62.2%
ValueCountFrequency (%)
0.141
 
0.5%
0.161
 
0.5%
0.182
 
0.9%
0.194
1.8%
0.23
1.4%
0.214
1.8%
0.221
 
0.5%
0.232
 
0.9%
0.243
1.4%
0.255
2.3%
ValueCountFrequency (%)
0.941
0.5%
0.911
0.5%
0.721
0.5%
0.712
0.9%
0.72
0.9%
0.671
0.5%
0.652
0.9%
0.641
0.5%
0.631
0.5%
0.622
0.9%

eosinophil_count_10e9_L
Real number (ℝ)

Zeros 

Distinct47
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.13414747
Minimum0
Maximum0.74
Zeros5
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2025-11-25T08:49:21.786378image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.02
Q10.05
median0.1
Q30.18
95-th percentile0.38
Maximum0.74
Range0.74
Interquartile range (IQR)0.13

Descriptive statistics

Standard deviation0.133508
Coefficient of variation (CV)0.9952331
Kurtosis5.742391
Mean0.13414747
Median Absolute Deviation (MAD)0.06
Skewness2.2085579
Sum29.11
Variance0.017824386
MonotonicityNot monotonic
2025-11-25T08:49:21.836754image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0.0418
 
8.3%
0.0717
 
7.8%
0.115
 
6.9%
0.0515
 
6.9%
0.0315
 
6.9%
0.0211
 
5.1%
0.1110
 
4.6%
0.0610
 
4.6%
0.1210
 
4.6%
0.088
 
3.7%
Other values (37)88
40.6%
ValueCountFrequency (%)
05
 
2.3%
0.013
 
1.4%
0.0211
5.1%
0.0315
6.9%
0.0418
8.3%
0.0515
6.9%
0.0610
4.6%
0.0717
7.8%
0.088
3.7%
0.094
 
1.8%
ValueCountFrequency (%)
0.741
0.5%
0.71
0.5%
0.681
0.5%
0.651
0.5%
0.621
0.5%
0.581
0.5%
0.541
0.5%
0.481
0.5%
0.461
0.5%
0.391
0.5%

basophil_count_10e9_L
Real number (ℝ)

Zeros 

Distinct14
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.031705069
Minimum0
Maximum0.19
Zeros13
Zeros (%)6.0%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2025-11-25T08:49:21.880180image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.02
median0.03
Q30.04
95-th percentile0.07
Maximum0.19
Range0.19
Interquartile range (IQR)0.02

Descriptive statistics

Standard deviation0.02421634
Coefficient of variation (CV)0.76380026
Kurtosis11.4033
Mean0.031705069
Median Absolute Deviation (MAD)0.01
Skewness2.550674
Sum6.88
Variance0.00058643113
MonotonicityNot monotonic
2025-11-25T08:49:21.918728image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0.0258
26.7%
0.0348
22.1%
0.0429
13.4%
0.0127
12.4%
0.0518
 
8.3%
013
 
6.0%
0.0610
 
4.6%
0.077
 
3.2%
0.122
 
0.9%
0.111
 
0.5%
Other values (4)4
 
1.8%
ValueCountFrequency (%)
013
 
6.0%
0.0127
12.4%
0.0258
26.7%
0.0348
22.1%
0.0429
13.4%
0.0518
 
8.3%
0.0610
 
4.6%
0.077
 
3.2%
0.081
 
0.5%
0.091
 
0.5%
ValueCountFrequency (%)
0.191
 
0.5%
0.151
 
0.5%
0.122
 
0.9%
0.111
 
0.5%
0.091
 
0.5%
0.081
 
0.5%
0.077
 
3.2%
0.0610
 
4.6%
0.0518
8.3%
0.0429
13.4%

mcv_fL
Real number (ℝ)

Distinct166
Distinct (%)76.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean96.740553
Minimum60.5
Maximum121.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2025-11-25T08:49:21.964830image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum60.5
5-th percentile77.26
Q189.3
median96.6
Q3106.4
95-th percentile112.82
Maximum121.1
Range60.6
Interquartile range (IQR)17.1

Descriptive statistics

Standard deviation11.520048
Coefficient of variation (CV)0.11908189
Kurtosis-0.11213617
Mean96.740553
Median Absolute Deviation (MAD)8.7
Skewness-0.43326315
Sum20992.7
Variance132.7115
MonotonicityNot monotonic
2025-11-25T08:49:22.015104image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
109.54
 
1.8%
92.34
 
1.8%
111.44
 
1.8%
95.63
 
1.4%
109.73
 
1.4%
87.32
 
0.9%
99.42
 
0.9%
100.22
 
0.9%
1032
 
0.9%
932
 
0.9%
Other values (156)189
87.1%
ValueCountFrequency (%)
60.51
0.5%
62.21
0.5%
671
0.5%
68.21
0.5%
69.51
0.5%
72.31
0.5%
73.91
0.5%
75.31
0.5%
76.11
0.5%
76.71
0.5%
ValueCountFrequency (%)
121.11
0.5%
117.11
0.5%
115.71
0.5%
114.71
0.5%
1141
0.5%
113.71
0.5%
113.61
0.5%
113.41
0.5%
1131
0.5%
112.92
0.9%

rdw_percent
Real number (ℝ)

Distinct58
Distinct (%)26.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.628111
Minimum11.9
Maximum23.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2025-11-25T08:49:22.065514image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum11.9
5-th percentile12.6
Q113.5
median14.3
Q315.1
95-th percentile18.34
Maximum23.9
Range12
Interquartile range (IQR)1.6

Descriptive statistics

Standard deviation1.805263
Coefficient of variation (CV)0.12341054
Kurtosis5.6324967
Mean14.628111
Median Absolute Deviation (MAD)0.8
Skewness2.0358278
Sum3174.3
Variance3.2589747
MonotonicityNot monotonic
2025-11-25T08:49:22.116793image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.812
 
5.5%
14.311
 
5.1%
13.310
 
4.6%
14.110
 
4.6%
14.29
 
4.1%
158
 
3.7%
13.48
 
3.7%
13.98
 
3.7%
15.28
 
3.7%
14.77
 
3.2%
Other values (48)126
58.1%
ValueCountFrequency (%)
11.91
 
0.5%
12.13
1.4%
12.21
 
0.5%
12.41
 
0.5%
12.53
1.4%
12.66
2.8%
12.71
 
0.5%
12.82
 
0.9%
12.94
1.8%
133
1.4%
ValueCountFrequency (%)
23.91
0.5%
21.91
0.5%
20.82
0.9%
20.51
0.5%
201
0.5%
19.82
0.9%
19.51
0.5%
18.91
0.5%
18.51
0.5%
18.32
0.9%

alt_U_L
Real number (ℝ)

High correlation 

Distinct42
Distinct (%)19.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.926267
Minimum6
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2025-11-25T08:49:22.164417image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile10
Q114
median17
Q324
95-th percentile41
Maximum98
Range92
Interquartile range (IQR)10

Descriptive statistics

Standard deviation13.499798
Coefficient of variation (CV)0.64511255
Kurtosis14.618813
Mean20.926267
Median Absolute Deviation (MAD)5
Skewness3.3227683
Sum4541
Variance182.24454
MonotonicityNot monotonic
2025-11-25T08:49:22.293503image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
1517
 
7.8%
1416
 
7.4%
1715
 
6.9%
1214
 
6.5%
1613
 
6.0%
1011
 
5.1%
2111
 
5.1%
1310
 
4.6%
189
 
4.1%
208
 
3.7%
Other values (32)93
42.9%
ValueCountFrequency (%)
62
 
0.9%
71
 
0.5%
82
 
0.9%
94
 
1.8%
1011
5.1%
118
3.7%
1214
6.5%
1310
4.6%
1416
7.4%
1517
7.8%
ValueCountFrequency (%)
982
0.9%
971
0.5%
711
0.5%
701
0.5%
641
0.5%
501
0.5%
462
0.9%
431
0.5%
412
0.9%
402
0.9%

ast_U_L
Real number (ℝ)

High correlation 

Distinct34
Distinct (%)15.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.705069
Minimum10
Maximum97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2025-11-25T08:49:22.338087image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile14.8
Q118
median21
Q325
95-th percentile33.2
Maximum97
Range87
Interquartile range (IQR)7

Descriptive statistics

Standard deviation8.3258645
Coefficient of variation (CV)0.36669629
Kurtosis30.009058
Mean22.705069
Median Absolute Deviation (MAD)4
Skewness4.0488585
Sum4927
Variance69.32002
MonotonicityNot monotonic
2025-11-25T08:49:22.379858image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
2219
 
8.8%
2018
 
8.3%
2118
 
8.3%
1717
 
7.8%
2314
 
6.5%
1913
 
6.0%
1513
 
6.0%
1813
 
6.0%
2511
 
5.1%
2411
 
5.1%
Other values (24)70
32.3%
ValueCountFrequency (%)
101
 
0.5%
123
 
1.4%
131
 
0.5%
146
 
2.8%
1513
6.0%
168
3.7%
1717
7.8%
1813
6.0%
1913
6.0%
2018
8.3%
ValueCountFrequency (%)
971
0.5%
551
0.5%
501
0.5%
481
0.5%
431
0.5%
411
0.5%
401
0.5%
391
0.5%
381
0.5%
351
0.5%

albumin_g_dL
Categorical

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing216
Missing (%)99.5%
Memory size13.6 KiB
41.0

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters4
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row41.0

Common Values

ValueCountFrequency (%)
41.01
 
0.5%
(Missing)216
99.5%

Length

2025-11-25T08:49:22.425292image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T08:49:22.459592image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
41.01
100.0%

Most occurring characters

ValueCountFrequency (%)
41
25.0%
11
25.0%
.1
25.0%
01
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3
75.0%
Other Punctuation1
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
41
33.3%
11
33.3%
01
33.3%
Other Punctuation
ValueCountFrequency (%)
.1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
41
25.0%
11
25.0%
.1
25.0%
01
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII4
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
41
25.0%
11
25.0%
.1
25.0%
01
25.0%

creatinine_umol_L
Real number (ℝ)

High correlation 

Distinct59
Distinct (%)27.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.866359
Minimum31
Maximum169
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2025-11-25T08:49:22.495536image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum31
5-th percentile44
Q154
median62
Q371
95-th percentile91
Maximum169
Range138
Interquartile range (IQR)17

Descriptive statistics

Standard deviation15.771975
Coefficient of variation (CV)0.24695278
Kurtosis8.6528075
Mean63.866359
Median Absolute Deviation (MAD)9
Skewness1.8734959
Sum13859
Variance248.75521
MonotonicityNot monotonic
2025-11-25T08:49:22.542238image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6211
 
5.1%
5410
 
4.6%
659
 
4.1%
668
 
3.7%
488
 
3.7%
577
 
3.2%
597
 
3.2%
527
 
3.2%
587
 
3.2%
637
 
3.2%
Other values (49)136
62.7%
ValueCountFrequency (%)
311
 
0.5%
391
 
0.5%
411
 
0.5%
422
 
0.9%
433
 
1.4%
444
1.8%
455
2.3%
462
 
0.9%
473
 
1.4%
488
3.7%
ValueCountFrequency (%)
1691
0.5%
1141
0.5%
1101
0.5%
992
0.9%
981
0.5%
961
0.5%
951
0.5%
941
0.5%
921
0.5%
912
0.9%

creatinine_clearance_mL_min
Real number (ℝ)

High correlation 

Distinct113
Distinct (%)52.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129.04147
Minimum40
Maximum243
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2025-11-25T08:49:22.590060image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile77.8
Q1100
median126
Q3153
95-th percentile192
Maximum243
Range203
Interquartile range (IQR)53

Descriptive statistics

Standard deviation37.493371
Coefficient of variation (CV)0.29055287
Kurtosis-0.35583415
Mean129.04147
Median Absolute Deviation (MAD)26
Skewness0.36660724
Sum28002
Variance1405.7529
MonotonicityNot monotonic
2025-11-25T08:49:22.641727image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1307
 
3.2%
1205
 
2.3%
935
 
2.3%
1275
 
2.3%
1005
 
2.3%
945
 
2.3%
905
 
2.3%
864
 
1.8%
1194
 
1.8%
1354
 
1.8%
Other values (103)168
77.4%
ValueCountFrequency (%)
401
0.5%
591
0.5%
601
0.5%
642
0.9%
652
0.9%
661
0.5%
681
0.5%
711
0.5%
771
0.5%
781
0.5%
ValueCountFrequency (%)
2431
0.5%
2251
0.5%
2171
0.5%
2091
0.5%
2051
0.5%
2031
0.5%
1992
0.9%
1981
0.5%
1971
0.5%
1961
0.5%

total_cholesterol_mmol_L
Real number (ℝ)

High correlation 

Distinct155
Distinct (%)71.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9320276
Minimum2.82
Maximum8.18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2025-11-25T08:49:22.693443image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2.82
5-th percentile3.43
Q14.28
median4.74
Q35.53
95-th percentile6.698
Maximum8.18
Range5.36
Interquartile range (IQR)1.25

Descriptive statistics

Standard deviation0.962825
Coefficient of variation (CV)0.1952189
Kurtosis-0.033836679
Mean4.9320276
Median Absolute Deviation (MAD)0.61
Skewness0.41501086
Sum1070.25
Variance0.92703198
MonotonicityNot monotonic
2025-11-25T08:49:22.744161image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.584
 
1.8%
3.743
 
1.4%
4.793
 
1.4%
4.073
 
1.4%
4.963
 
1.4%
6.043
 
1.4%
4.383
 
1.4%
4.183
 
1.4%
3.693
 
1.4%
5.323
 
1.4%
Other values (145)186
85.7%
ValueCountFrequency (%)
2.821
 
0.5%
2.851
 
0.5%
2.891
 
0.5%
3.081
 
0.5%
3.191
 
0.5%
3.341
 
0.5%
3.392
0.9%
3.41
 
0.5%
3.433
1.4%
3.571
 
0.5%
ValueCountFrequency (%)
8.181
0.5%
7.341
0.5%
7.092
0.9%
6.971
0.5%
6.821
0.5%
6.811
0.5%
6.791
0.5%
6.781
0.5%
6.732
0.9%
6.691
0.5%
Distinct108
Distinct (%)49.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4440553
Minimum0.74
Maximum4.18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2025-11-25T08:49:22.790920image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.74
5-th percentile0.938
Q11.22
median1.41
Q31.61
95-th percentile2.072
Maximum4.18
Range3.44
Interquartile range (IQR)0.39

Descriptive statistics

Standard deviation0.39027905
Coefficient of variation (CV)0.270266
Kurtosis10.553597
Mean1.4440553
Median Absolute Deviation (MAD)0.19
Skewness1.9892542
Sum313.36
Variance0.15231774
MonotonicityNot monotonic
2025-11-25T08:49:22.839033image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.277
 
3.2%
1.246
 
2.8%
1.466
 
2.8%
1.195
 
2.3%
1.265
 
2.3%
1.555
 
2.3%
1.255
 
2.3%
1.525
 
2.3%
1.644
 
1.8%
1.454
 
1.8%
Other values (98)165
76.0%
ValueCountFrequency (%)
0.741
0.5%
0.751
0.5%
0.811
0.5%
0.831
0.5%
0.851
0.5%
0.881
0.5%
0.891
0.5%
0.911
0.5%
0.922
0.9%
0.931
0.5%
ValueCountFrequency (%)
4.181
0.5%
2.661
0.5%
2.421
0.5%
2.391
0.5%
2.31
0.5%
2.281
0.5%
2.231
0.5%
2.181
0.5%
2.161
0.5%
2.091
0.5%

ldl_cholesterol_mmol_L_consolidated
Real number (ℝ)

High correlation 

Distinct150
Distinct (%)69.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1781567
Minimum1.32
Maximum5.57
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2025-11-25T08:49:22.887933image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.32
5-th percentile1.806
Q12.62
median3.15
Q33.74
95-th percentile4.744
Maximum5.57
Range4.25
Interquartile range (IQR)1.12

Descriptive statistics

Standard deviation0.86492577
Coefficient of variation (CV)0.27214699
Kurtosis-0.2915476
Mean3.1781567
Median Absolute Deviation (MAD)0.55
Skewness0.25608402
Sum689.66
Variance0.74809659
MonotonicityNot monotonic
2025-11-25T08:49:22.934344image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.664
 
1.8%
3.324
 
1.8%
2.93
 
1.4%
3.673
 
1.4%
2.753
 
1.4%
3.693
 
1.4%
2.683
 
1.4%
3.343
 
1.4%
3.923
 
1.4%
2.513
 
1.4%
Other values (140)185
85.3%
ValueCountFrequency (%)
1.321
0.5%
1.381
0.5%
1.512
0.9%
1.531
0.5%
1.621
0.5%
1.631
0.5%
1.661
0.5%
1.681
0.5%
1.731
0.5%
1.751
0.5%
ValueCountFrequency (%)
5.571
0.5%
5.231
0.5%
5.221
0.5%
5.071
0.5%
5.031
0.5%
5.021
0.5%
4.871
0.5%
4.841
0.5%
4.811
0.5%
4.82
0.9%

Interactions

2025-11-25T08:49:19.705499image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:08.224455image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:08.923195image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:09.550481image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:10.223983image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:10.838587image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:11.442049image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:12.026933image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:12.744797image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:13.381611image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:14.029905image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:14.760872image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:15.356253image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:15.952709image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:16.594088image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:17.157783image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:17.768273image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:18.409570image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:19.081995image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:19.735748image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:08.291155image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:08.953031image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:09.578504image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:10.253978image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:10.868463image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:11.470657image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:12.060054image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:12.776349image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:13.412620image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:14.060776image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:14.790057image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:15.387724image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:15.980347image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:16.620933image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:17.187573image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:17.800058image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:18.437900image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:19.112295image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:19.769061image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:08.330265image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:08.987496image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:09.611468image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:10.287022image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:10.900823image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:11.502419image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:12.095571image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:12.810911image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:13.446491image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:14.096576image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:14.824730image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:15.420425image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:16.011871image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:16.650663image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:17.223117image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:17.835227image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:18.470301image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:19.149010image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:19.799952image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:08.365583image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:09.019717image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:09.640513image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:10.318965image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:10.929669image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:11.532515image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:12.128233image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:12.843939image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:13.477626image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:14.128769image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:14.853774image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:15.451019image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:16.039612image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:16.678749image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:17.252640image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:17.869060image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:18.581320image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:19.180900image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:19.829608image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:08.436411image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:09.054201image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:09.672439image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:10.351023image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:10.962601image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:11.563620image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:12.161757image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:12.877950image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:13.511956image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:14.166203image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:14.887131image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:15.482831image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:16.069891image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:16.709813image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:17.285730image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:17.903894image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:18.613031image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:19.214882image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:19.859509image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:08.492856image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:09.086113image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:09.702834image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:10.383395image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:10.992985image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:11.593332image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:12.192274image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:12.910754image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:13.544719image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:14.198444image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:14.916151image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:15.513013image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:16.099488image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:16.738802image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:17.316736image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:17.938274image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:18.643792image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:19.246373image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:19.886817image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:08.522025image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:09.117422image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:09.733532image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:10.413617image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:11.023381image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:11.620953image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:12.309212image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:12.941341image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:13.576948image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:14.229592image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:14.944000image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:15.541821image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:16.126196image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:16.765809image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:17.345418image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:17.969876image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:18.672625image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:19.276098image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:19.919097image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:08.553701image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:09.151815image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:09.765655image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:10.448231image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:11.057005image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:11.652563image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:12.344568image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:12.976574image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:13.612321image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:14.265499image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:14.977011image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:15.574109image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:16.159457image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:16.798539image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:17.382566image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:18.006208image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:18.704584image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:19.309620image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:19.952874image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:08.587555image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:09.188943image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:09.799067image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:10.481878image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:11.093654image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:11.687986image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:12.381587image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:13.014161image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:13.650587image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:14.303052image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:15.010361image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:15.607690image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:16.193617image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:16.831255image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:17.417484image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:18.042219image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:18.740412image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:19.344864image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:19.984988image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:08.619782image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:09.223134image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:09.831878image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:10.517705image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:11.125497image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:11.720891image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:12.415604image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:13.048021image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:13.684479image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:14.428241image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:15.044178image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:15.641871image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:16.225035image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:16.863778image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:17.452252image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:18.077503image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:18.773493image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:19.380638image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:20.018571image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:08.653238image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:09.259411image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:09.863723image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:10.553156image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:11.160799image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:11.755347image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:12.451908image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:13.085202image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:13.723312image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:14.462023image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:15.078003image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:15.677176image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:16.257195image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:16.895779image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:17.485342image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:18.113840image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:18.808042image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:19.415119image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:20.049772image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:08.683901image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:09.291369image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:09.892802image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:10.585642image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:11.192675image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:11.786139image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:12.485228image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:13.118704image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:13.757830image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:14.496280image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:15.109030image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:15.709977image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:16.284610image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:16.924469image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:17.517881image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:18.147424image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:18.837701image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:19.449038image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:20.079778image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:08.714188image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:09.324817image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:09.922321image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:10.616896image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:11.223139image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:11.816226image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:12.518806image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:13.152703image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:13.802119image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:14.529338image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:15.139810image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:15.741966image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:16.314821image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:16.954543image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:17.549123image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:18.181638image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:18.869912image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:19.482358image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:20.108038image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:08.742856image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:09.354541image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:09.951601image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:10.647010image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:11.252539image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:11.844202image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:12.547459image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:13.182654image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:13.832981image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:14.560791image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:15.170322image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:15.769234image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:16.340841image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:16.981683image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:17.577867image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:18.212519image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:18.897239image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:19.512887image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:20.139553image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:08.770738image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:09.386440image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:09.979561image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:10.676666image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:11.281244image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:11.873004image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:12.577841image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:13.214676image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:13.863426image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:14.590740image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:15.197886image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:15.796153image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:16.367144image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:17.008857image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:17.605170image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:18.242523image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:18.926402image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:19.542155image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:20.169611image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:08.801934image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:09.418495image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:10.009985image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:10.709450image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:11.314184image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:11.905169image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:12.610861image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:13.248564image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:13.897563image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:14.625120image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:15.230102image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:15.827177image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:16.397565image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:17.037916image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:17.637169image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:18.275083image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:18.957092image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:19.575833image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:20.207635image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:08.835778image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:09.453994image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:10.132991image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:10.745580image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:11.348453image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:11.937677image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:12.648194image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:13.286375image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:13.933355image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:14.660892image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:15.264603image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:15.860895image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:16.429212image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:17.071646image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:17.673430image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:18.310532image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:18.992124image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:19.610839image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:20.245955image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:08.864800image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:09.484732image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:10.162821image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:10.775401image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:11.380850image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:11.966367image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:12.679590image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:13.318885image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:13.966488image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:14.695511image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:15.295348image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:15.891808image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:16.537792image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:17.100728image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:17.704221image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:18.343056image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:19.020859image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:19.644370image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:20.349136image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:08.896450image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:09.521865image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:10.196371image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:10.809681image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:11.413353image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:11.999359image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:12.714963image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:13.353436image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:13.999244image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:14.728972image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:15.328054image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:15.923850image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:16.567272image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:17.130848image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:17.739730image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:18.378734image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:19.052857image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:49:19.676958image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-11-25T08:49:22.974597image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Age (at enrolment)Sexalt_U_Last_U_Lbasophil_count_10e9_Lcd4_count_cells_uLcreatinine_clearance_mL_mincreatinine_umol_Leosinophil_count_10e9_Lhdl_cholesterol_mmol_L_consolidatedhemoglobin_g_dLhiv_vl_copies_mLldl_cholesterol_mmol_L_consolidatedlymphocyte_count_10e9_Lmcv_fLmonocyte_count_10e9_Lneutrophil_count_10e9_Lplatelet_count_10e9_Lrdw_percenttotal_cholesterol_mmol_Lwbc_count_10e9_L
Age (at enrolment)1.0000.142-0.026-0.0110.0250.025-0.2970.212-0.0590.1590.0510.1610.114-0.0180.0950.064-0.095-0.055-0.0990.198-0.047
Sex0.1421.0000.0000.2360.0930.2270.3090.5890.1920.1370.5950.0000.0000.1480.1890.0930.1880.3460.1360.0120.261
alt_U_L-0.0260.0001.0000.737-0.0810.122-0.0330.167-0.1090.0110.3160.000-0.0330.044-0.0940.091-0.024-0.047-0.166-0.0270.025
ast_U_L-0.0110.2360.7371.000-0.0730.086-0.2360.186-0.0720.1300.1690.000-0.080-0.098-0.146-0.003-0.076-0.060-0.078-0.065-0.104
basophil_count_10e9_L0.0250.093-0.081-0.0731.0000.191-0.1300.1420.2420.028-0.0250.000-0.0120.243-0.0530.1740.1510.0680.0820.0020.254
cd4_count_cells_uL0.0250.2270.1220.0860.1911.000-0.002-0.0380.093-0.006-0.0400.000-0.0240.680-0.0650.3950.3890.2510.054-0.0140.617
creatinine_clearance_mL_min-0.2970.309-0.033-0.236-0.130-0.0021.000-0.676-0.016-0.181-0.1710.000-0.0370.039-0.1460.0510.1270.2520.113-0.0770.132
creatinine_umol_L0.2120.5890.1670.1860.142-0.038-0.6761.0000.1130.0060.4490.0000.068-0.0220.0310.014-0.136-0.278-0.1910.098-0.105
eosinophil_count_10e9_L-0.0590.192-0.109-0.0720.2420.093-0.0160.1131.000-0.0650.0880.096-0.0610.150-0.0440.1170.0660.043-0.056-0.0750.177
hdl_cholesterol_mmol_L_consolidated0.1590.1370.0110.1300.028-0.006-0.1810.006-0.0651.000-0.0640.0480.022-0.1280.041-0.042-0.028-0.0650.0560.174-0.104
hemoglobin_g_dL0.0510.5950.3160.169-0.025-0.040-0.1710.4490.088-0.0641.0000.1170.1380.0190.196-0.090-0.014-0.340-0.4830.1500.018
hiv_vl_copies_mL0.1610.0000.0000.0000.0000.0000.0000.0000.0960.0480.1171.0000.0000.0000.0000.1280.0110.2930.0000.0000.055
ldl_cholesterol_mmol_L_consolidated0.1140.000-0.033-0.080-0.012-0.024-0.0370.068-0.0610.0220.1380.0001.0000.0180.0560.0250.0280.120-0.1260.9370.033
lymphocyte_count_10e9_L-0.0180.1480.044-0.0980.2430.6800.039-0.0220.150-0.1280.0190.0000.0181.0000.0100.4090.3070.2510.0270.0210.724
mcv_fL0.0950.189-0.094-0.146-0.053-0.065-0.1460.031-0.0440.0410.1960.0000.0560.0101.0000.0560.014-0.106-0.1420.0710.008
monocyte_count_10e9_L0.0640.0930.091-0.0030.1740.3950.0510.0140.117-0.042-0.0900.1280.0250.4090.0561.0000.4350.3450.0970.0230.598
neutrophil_count_10e9_L-0.0950.188-0.024-0.0760.1510.3890.127-0.1360.066-0.028-0.0140.0110.0280.3070.0140.4351.0000.3080.0030.0130.817
platelet_count_10e9_L-0.0550.346-0.047-0.0600.0680.2510.252-0.2780.043-0.065-0.3400.2930.1200.251-0.1060.3450.3081.0000.1280.1190.369
rdw_percent-0.0990.136-0.166-0.0780.0820.0540.113-0.191-0.0560.056-0.4830.000-0.1260.027-0.1420.0970.0030.1281.000-0.103-0.012
total_cholesterol_mmol_L0.1980.012-0.027-0.0650.002-0.014-0.0770.098-0.0750.1740.1500.0000.9370.0210.0710.0230.0130.119-0.1031.0000.018
wbc_count_10e9_L-0.0470.2610.025-0.1040.2540.6170.132-0.1050.177-0.1040.0180.0550.0330.7240.0080.5980.8170.369-0.0120.0181.000

Missing values

2025-11-25T08:49:20.429801image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-25T08:49:20.551905image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-11-25T08:49:20.619787image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Age (at enrolment)SexBMI (kg/m²)study_weekcd4_count_cells_uLhiv_vl_copies_mLart_statushemoglobin_g_dLwbc_count_10e9_Lplatelet_count_10e9_Lneutrophil_count_10e9_Llymphocyte_count_10e9_Lmonocyte_count_10e9_Leosinophil_count_10e9_Lbasophil_count_10e9_Lmcv_fLrdw_percentalt_U_Last_U_Lalbumin_g_dLcreatinine_umol_Lcreatinine_clearance_mL_mintotal_cholesterol_mmol_Lhdl_cholesterol_mmol_L_consolidatedldl_cholesterol_mmol_L_consolidated
030.0FemaleNaNNaN1020.00.0Positive10.95.21390.01.842.810.470.070.0383.618.116.025.0NaN49.0145.06.792.304.35
153.0FemaleNaNNaN446.00.0Positive13.53.68234.01.371.790.380.110.02112.816.18.015.0NaN74.065.04.931.493.10
236.0FemaleNaNNaN1054.040.0Positive13.37.71344.02.334.700.560.110.0281.513.217.017.0NaN59.0199.05.191.253.19
347.0FemaleNaNNaN989.00.0Positive10.86.35257.03.422.340.520.040.03107.215.511.012.0NaN53.0181.06.691.524.80
434.0MaleNaNNaN160.040.0Positive11.24.17343.02.211.330.440.180.02102.015.041.032.0NaN66.0145.03.191.181.51
540.0FemaleNaNNaN989.040.0Positive15.17.09319.02.274.180.350.280.00101.214.114.017.0NaN52.0177.05.581.063.66
635.0MaleNaNNaN453.00.0Positive17.44.66229.02.291.480.490.370.0394.913.730.024.0NaN98.0113.05.521.673.70
730.0MaleNaNNaN288.00.0Positive17.74.12240.02.141.430.440.100.0187.513.864.043.0NaN81.084.04.561.273.12
844.0FemaleNaNNaN907.00.0Positive12.34.77230.01.492.740.460.050.0292.915.025.031.0NaN74.0119.04.121.232.25
936.0MaleNaNNaN509.040.0Positive15.54.72186.02.851.630.190.030.0297.112.928.026.0NaN59.0154.04.261.772.21
Age (at enrolment)SexBMI (kg/m²)study_weekcd4_count_cells_uLhiv_vl_copies_mLart_statushemoglobin_g_dLwbc_count_10e9_Lplatelet_count_10e9_Lneutrophil_count_10e9_Llymphocyte_count_10e9_Lmonocyte_count_10e9_Leosinophil_count_10e9_Lbasophil_count_10e9_Lmcv_fLrdw_percentalt_U_Last_U_Lalbumin_g_dLcreatinine_umol_Lcreatinine_clearance_mL_mintotal_cholesterol_mmol_Lhdl_cholesterol_mmol_L_consolidatedldl_cholesterol_mmol_L_consolidated
20746.0MaleNaNNaN190.040.0Positive14.34.36225.01.312.760.240.020.0386.315.340.030.0NaN62.0106.04.621.042.94
20833.0MaleNaNNaN596.00.0Positive15.54.45237.02.501.640.270.020.0284.615.297.050.0NaN89.0131.06.041.584.51
20943.0FemaleNaNNaN541.040.0Positive12.84.58403.02.132.020.390.040.00113.615.227.020.0NaN45.0177.06.241.604.55
21042.0MaleNaNNaN530.00.0Positive17.66.31245.03.702.210.340.020.04111.414.339.022.0NaN81.0119.06.731.275.07
21139.0FemaleNaNNaN390.00.0Positive11.64.29254.02.271.570.400.020.03102.920.014.014.0NaN44.0172.04.461.762.85
21239.0MaleNaNNaN415.00.0Positive14.04.42192.02.361.740.260.040.0384.814.218.025.0NaN62.0109.04.711.713.18
21357.0FemaleNaNNaN786.063.0Positive11.38.15447.03.633.530.630.350.0189.213.816.017.0NaN71.0120.06.031.414.70
21461.0MaleNaNNaN672.00.0Positive16.36.04320.02.272.930.360.300.19110.015.413.021.0NaN60.097.04.331.522.64
21537.0FemaleNaNNaN520.00.0Positive12.04.22248.02.131.590.380.100.03101.013.816.022.0NaN48.0127.05.841.434.39
21639.0FemaleNaNNaN888.040.0Positive12.94.22210.01.552.200.350.060.0590.013.946.032.0NaN66.090.06.252.663.60